# machine-translation-nar-en-ru-0002¶

## Use Case and High-Level Description¶

This is an English-Russian machine translation model based on non-autoregressive Transformer topology.

Tokenization occurs using the SentencePieceBPETokenizer (see the demo code for implementation details) and is enclosed in tokenizer_src and tokenizer_tgt folders.

Metric

Value

GOps

23.17

MParams

69.29

Source framework

PyTorch*

## Accuracy¶

The quality metrics were calculated on the wmt19-ru-en dataset (“test” split in lower case).

Metric

Value

BLEU

22.7 %

Use accuracy_check [...] --model_attributes <path_to_folder_with_downloaded_model> to specify the path to additional model attributes. path_to_folder_with_downloaded_model is a path to the folder, where the current model is downloaded by Model Downloader tool.

## Input¶

name: tokens shape: 1, 192 description: sequence of tokens (integer values) representing the tokenized sentence. The sequence structure is as follows (<s>, </s> and <pad> should be replaced by corresponding token IDs as specified by the dictionary): <s> + tokenized sentence + </s> + (<pad> tokens to pad to the maximum sequence length of 192)

## Output¶

name: pred shape: 1, 192 description: sequence of tokens (integer values) representing the tokenized translation. The sequence structure is as follows (<s>, </s> and <pad> should be replaced by corresponding token IDs as specified by the dictionary): <s> + tokenized sentence + </s> + (<pad> tokens to pad to the maximum sequence length of 192)

## Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: